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Adil Zouitine PRO

AdilZtn
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https://boring-guy.sh/

AI & ML interests

Reinforcement learning, Robotics, Computer vision, Scaling

Recent Activity

new activity about 21 hours ago
AdilZtn/act_grab_red_cube_main_long:Migrate policy to PolicyProcessorPipeline system
new activity about 21 hours ago
AdilZtn/act_grab_red_cube_main_long:Migrate policy to PolicyProcessorPipeline system šŸ¤– **Automated Policy Migration to PolicyProcessorPipeline** This PR migrates your model to the new LeRobot policy format using the modern PolicyProcessorPipeline architecture. ## What Changed ### āœØ **New Architecture - PolicyProcessorPipeline** Your model now uses external PolicyProcessorPipeline components for data processing instead of built-in normalization layers. This provides: - **Modularity**: Separate preprocessing and postprocessing pipelines - **Flexibility**: Easy to swap, configure, and debug processing steps - **Compatibility**: Works with the latest LeRobot ecosystem ### šŸ”§ **Normalization Extraction** We've extracted normalization statistics from your model's state_dict and removed the built-in normalization layers: - **Extracted patterns**: `normalize_inputs.*`, `unnormalize_outputs.*`, `normalize.*`, `unnormalize.*`, `input_normalizer.*`, `output_normalizer.*` - **Statistics preserved**: Mean, std, min, max values for all features - **Clean model**: State dict now contains only core model weights ### šŸ“¦ **Files Added** - **preprocessor_config.json**: Configuration for input preprocessing pipeline - **postprocessor_config.json**: Configuration for output postprocessing pipeline - **model.safetensors**: Clean model weights without normalization layers - **config.json**: Updated model configuration - **train_config.json**: Training configuration - **README.md**: Updated model card with migration information ### šŸš€ **Benefits** - **Backward Compatible**: Your model behavior remains identical - **Future Ready**: Compatible with latest LeRobot features and updates - **Debuggable**: Easy to inspect and modify processing steps - **Portable**: Processors can be shared and reused across models ### šŸ’» **Usage** ```python # Load your migrated model from lerobot.policies import get_policy_class from lerobot.processor import PolicyProcessorPipeline # The preprocessor and postprocessor are now external preprocessor = PolicyProcessorPipeline.from_pretrained("your-model-repo", config_filename="preprocessor_config.json") postprocessor = PolicyProcessorPipeline.from_pretrained("your-model-repo", config_filename="postprocessor_config.json") policy = get_policy_class("your-policy-type").from_pretrained("your-model-repo") # Process data through the pipeline processed_batch = preprocessor(raw_batch) action = policy(processed_batch) final_action = postprocessor(action) ``` *Generated automatically by the LeRobot policy migration script*
new activity about 21 hours ago
AdilZtn/act_grab_red_cube_main_long:Migrate policy to PolicyProcessorPipeline system šŸ¤– **Automated Policy Migration to PolicyProcessorPipeline** This PR migrates your model to the new LeRobot policy format using the modern PolicyProcessorPipeline architecture. ## What Changed ### āœØ **New Architecture - PolicyProcessorPipeline** Your model now uses external PolicyProcessorPipeline components for data processing instead of built-in normalization layers. This provides: - **Modularity**: Separate preprocessing and postprocessing pipelines - **Flexibility**: Easy to swap, configure, and debug processing steps - **Compatibility**: Works with the latest LeRobot ecosystem ### šŸ”§ **Normalization Extraction** We've extracted normalization statistics from your model's state_dict and removed the built-in normalization layers: - **Extracted patterns**: `normalize_inputs.*`, `unnormalize_outputs.*`, `normalize.*`, `unnormalize.*`, `input_normalizer.*`, `output_normalizer.*` - **Statistics preserved**: Mean, std, min, max values for all features - **Clean model**: State dict now contains only core model weights ### šŸ“¦ **Files Added** - **preprocessor_config.json**: Configuration for input preprocessing pipeline - **postprocessor_config.json**: Configuration for output postprocessing pipeline - **model.safetensors**: Clean model weights without normalization layers - **config.json**: Updated model configuration - **train_config.json**: Training configuration - **README.md**: Updated model card with migration information ### šŸš€ **Benefits** - **Backward Compatible**: Your model behavior remains identical - **Future Ready**: Compatible with latest LeRobot features and updates - **Debuggable**: Easy to inspect and modify processing steps - **Portable**: Processors can be shared and reused across models ### šŸ’» **Usage** ```python # Load your migrated model from lerobot.policies import get_policy_class from lerobot.processor import PolicyProcessorPipeline # The preprocessor and postprocessor are now external preprocessor = PolicyProcessorPipeline.from_pretrained("your-model-repo", config_filename="preprocessor_config.json") postprocessor = PolicyProcessorPipeline.from_pretrained("your-model-repo", config_filename="postprocessor_config.json") policy = get_policy_class("your-policy-type").from_pretrained("your-model-repo") # Process data through the pipeline processed_batch = preprocessor(raw_batch) action = policy(processed_batch) final_action = postprocessor(action) ``` *Generated automatically by the LeRobot policy migration script*
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